In this notebook, I am going to do some EDA and visualization on vaccine dataset.

import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
%config InlineBackend.figure_format='retina'
%matplotlib inline

vaccine=pd.read_csv('../clean_data/vaccine_population.csv')

vaccine.shape

(52, 11)

#normalize the total first dose allocation, use it devided by total population per state

vaccine['al_over_po']=vaccine['total_first_allocation']/vaccine['2019_population']

vaccine.head()

	jurisdiction	2019_population	hhs_region	doses_allocated_12_14	doses_allocated_12_21	doses_allocated_12_28	doses_allocated_01_04	doses_allocated_01_10	doses_allocated_01_18	doses_allocated_01_25	total_first_allocation	al_over_po
0	Alabama	4903185	Region 4	40950	113550	67125	58250	58350	60825	60825	459875	0.093791
1	Alaska	731545	Region 10	35100	26800	0	52900	0	0	0	114800	0.156928
2	Arizona	7278717	Region 9	58500	161325	94925	83025	83225	86300	86300	653600	0.089796
3	Arkansas	3017804	Region 6	25350	69625	41100	36125	36225	37125	37125	282675	0.093669
4	California	39512223	Region 9	327600	905625	529675	463450	465325	485800	485800	3663275	0.092712

sns.boxplot(x=vaccine['total_first_allocation'],color='skyblue')
plt.ticklabel_format(style='plain',axis='x')
plt.title('Total First Allocation Per Jurisdiction', size=20)
plt.xlabel('Total First Allocation',size=15);

# there are 4 jurisdictions has way more allocations than other jurisdiction

vaccine.sort_values(by=['total_first_allocation'], ascending=False)[:4]

# California has the most allocation, then it's Texas, Florida and New York

	jurisdiction	2019_population	hhs_region	doses_allocated_12_14	doses_allocated_12_21	doses_allocated_12_28	doses_allocated_01_04	doses_allocated_01_10	doses_allocated_01_18	doses_allocated_01_25	total_first_allocation	al_over_po
4	California	39512223	Region 9	327600	905625	529675	463450	465325	485800	485800	3663275	0.092712
43	Texas	28995881	Region 6	224250	620400	362875	317425	319000	332850	332850	2509650	0.086552
9	Florida	21477737	Region 4	179400	495625	289925	253350	254825	266100	266100	2005325	0.093368
32	New York	19453561	Region 2	169650	466125	273650	239025	239525	250400	250400	1888775	0.097091

plt.figure(figsize=(15,6))
sns.barplot(data=vaccine,
            y=vaccine['al_over_po'],x=vaccine['jurisdiction'],
            palette='Paired')#,order=vaccine_sort.jurisdiction)
#plt.ticklabel_format(style='plain',axis='y')
plt.xlabel("Jurisdiction",size=25)
plt.ylabel("Allocation over Population",size=25)
plt.title('Total First Allocation Per Population in Each State',size=30)

plt.xticks(rotation=80);

#total first allocation per population in each state with alphabet oder

plt.figure(figsize=(15,6))
sns.barplot(data=vaccine,y=vaccine['total_first_allocation'],x=vaccine['jurisdiction'],
            palette='Paired')
plt.ticklabel_format(style='plain',axis='y')
plt.xlabel("Jurisdiction",size=25)
plt.ylabel("Total First Allocation",size=25)
plt.title('Total First Allocation Per Jurisdiction',size=30)

plt.xticks(rotation=80);

# Total first allocation per jurisdiction with alphabet order

sns.set(style='white')

ax=vaccine.drop(['total_first_allocation','2019_population','al_over_po'],axis=1).\
plot.barh(stacked=True,figsize=(15,20))

plt.ticklabel_format(style='plain',axis='x')
ax.set_yticklabels(vaccine.jurisdiction)
plt.ylabel("Jurisdiction",size=25)
plt.xlabel("Total First Allocation",size=25)
plt.title('Total First Allocation Per Jurisdiction',size=30);

#total first allocation per jurisdiction on a stacked bar chart, with alphabet order

Plot some graphies in the oder of total allocations

vaccine_sort=vaccine.sort_values(by=['total_first_allocation'], 
                                 ascending=False).drop(columns=['2019_population'])
vaccine_sort.head()

	jurisdiction	hhs_region	doses_allocated_12_14	doses_allocated_12_21	doses_allocated_12_28	doses_allocated_01_04	doses_allocated_01_10	doses_allocated_01_18	doses_allocated_01_25	total_first_allocation	al_over_po
4	California	Region 9	327600	905625	529675	463450	465325	485800	485800	3663275	0.092712
43	Texas	Region 6	224250	620400	362875	317425	319000	332850	332850	2509650	0.086552
9	Florida	Region 4	179400	495625	289925	253350	254825	266100	266100	2005325	0.093368
32	New York	Region 2	169650	466125	273650	239025	239525	250400	250400	1888775	0.097091
38	Pennsylvania	Region 3	111150	305375	179600	156875	157175	164000	164000	1238175	0.096717

plt.figure(figsize=(15,6))
sns.barplot(data=vaccine_sort,
            y=vaccine_sort['al_over_po'],x=vaccine_sort['jurisdiction'],
            palette='Paired')#,order=vaccine_sort.jurisdiction)
#plt.ticklabel_format(style='plain',axis='y')
plt.xlabel("Jurisdiction",size=25)
plt.ylabel("Allocation over Population",size=25)
plt.title('Total First Allocation Per Population in Each State',size=30)

plt.xticks(rotation=80);
plt.savefig('../images/total_allocation_per_population_state.png')
#total first dose allocation per population with the total allocation in descending order

plt.figure(figsize=(15,6))
sns.barplot(data=vaccine_sort.drop('al_over_po',axis=1),
            y=vaccine_sort['total_first_allocation'],x=vaccine_sort['jurisdiction'],
            palette='Paired')#,order=vaccine_sort.jurisdiction)
plt.ticklabel_format(style='plain',axis='y')
plt.xlabel("Jurisdiction",size=25)
plt.ylabel("Total First Allocation",size=25)
plt.title('Total First Allocation Per Jurisdiction',size=30)

plt.xticks(rotation=80);

#total first allocation per jurisdiction with the total allocation in descending order

vaccine_sort_10=vaccine_sort[:10].reset_index(drop=True)
vaccine_sort_10.head()

	jurisdiction	hhs_region	doses_allocated_12_14	doses_allocated_12_21	doses_allocated_12_28	doses_allocated_01_04	doses_allocated_01_10	doses_allocated_01_18	doses_allocated_01_25	total_first_allocation	al_over_po
0	California	Region 9	327600	905625	529675	463450	465325	485800	485800	3663275	0.092712
1	Texas	Region 6	224250	620400	362875	317425	319000	332850	332850	2509650	0.086552
2	Florida	Region 4	179400	495625	289925	253350	254825	266100	266100	2005325	0.093368
3	New York	Region 2	169650	466125	273650	239025	239525	250400	250400	1888775	0.097091
4	Pennsylvania	Region 3	111150	305375	179600	156875	157175	164000	164000	1238175	0.096717

plt.figure(figsize=(15,6))

g=sns.barplot(data=vaccine_sort_10,y=vaccine_sort_10['total_first_allocation'],
              x=vaccine_sort_10['jurisdiction'],palette='Paired')

plt.ticklabel_format(style='plain',axis='y')
plt.xlabel("Jurisdiction",size=25)
plt.ylabel("Total First Allocation",size=25)
plt.title('Total First Allocation Per Jurisdiction For Top 10 States',size=30)
plt.ylim([0,4663275])
plt.xlim([-0.8,10])
plt.xticks(rotation=30,size=15)

#n=-1
for index, row in vaccine_sort_10.iterrows():
    #n+=1
    g.text(row.name+0.2,row.total_first_allocation+80000,
           'Vacc. Alloc'' ''{0:.1f}K'.format(row.total_first_allocation/1000),
           color='black',ha='center',rotation=20)

    

vaccine_sort_no_total=vaccine.sort_values(by=['total_first_allocation'], \
                                          ascending=True).drop(['total_first_allocation','2019_population','al_over_po'],axis=1)
vaccine_sort_no_total=vaccine_sort_no_total.set_index('jurisdiction')

sns.set(style='white')
ax=vaccine_sort_no_total.plot(kind='barh',stacked=True,figsize=(15,20))
plt.ticklabel_format(style='plain',axis='x')
ax.set_yticklabels(vaccine_sort_no_total.index)
plt.ylabel("Jurisdiction",size=25)
plt.xlabel("Total First Allocation",size=25)
plt.title('Total First Allocation Per Jurisdiction',size=30)
plt.legend(fontsize=18,loc='center right');

#total first allocation per jurisdiction with total allocation in descending order

add population into graphies

vaccine.tail()

	jurisdiction	2019_population	hhs_region	doses_allocated_12_14	doses_allocated_12_21	doses_allocated_12_28	doses_allocated_01_04	doses_allocated_01_10	doses_allocated_01_18	doses_allocated_01_25	total_first_allocation	al_over_po
47	Washington	7614893	Region 10	62400	172850	101825	88950	101825	93300	93300	714450	0.093823
48	West Virginia	1792147	Region 3	16575	44300	25925	22900	23000	23600	23600	179900	0.100382
49	Wisconsin	5822434	Region 5	49725	136100	79750	69800	70000	73750	73750	552875	0.094956
50	Wyoming	578759	Region 8	4875	13900	8375	7400	7400	7600	7600	57150	0.098746
51	Puerto Rico	3193694	Region 2	30225	81850	48200	42250	42350	44325	44325	333525	0.104432

# make a dataframe with only the state, population and total allocation

state_pop_vac=vaccine[['jurisdiction','2019_population','total_first_allocation']].copy()
state_pop_vac.head()

	jurisdiction	2019_population	total_first_allocation
0	Alabama	4903185	459875
1	Alaska	731545	114800
2	Arizona	7278717	653600
3	Arkansas	3017804	282675
4	California	39512223	3663275

df = pd.melt(state_pop_vac,id_vars='jurisdiction', var_name='pop_vac',value_name='data')
df.head()

	jurisdiction	pop_vac	data
0	Alabama	2019_population	4903185
1	Alaska	2019_population	731545
2	Arizona	2019_population	7278717
3	Arkansas	2019_population	3017804
4	California	2019_population	39512223

g = sns.catplot(x='jurisdiction',y='data',hue='pop_vac',data=df,
                saturation=1,kind='bar',ci=None,height=5, aspect=2)
plt.xticks(rotation=80);

# putting the population and alloction on the same scale is a bad idea

---

state_pop_vac_sort=state_pop_vac.sort_values(by=['total_first_allocation'],ascending=False)
state_pop_vac_sort.head()

	jurisdiction	2019_population	total_first_allocation
4	California	39512223	3663275
43	Texas	28995881	2509650
9	Florida	21477737	2005325
32	New York	19453561	1888775
38	Pennsylvania	12801989	1238175

state_pop_vac_sort.jurisdiction.count()

52

#convert to tuple
population=tuple(list(state_pop_vac_sort['2019_population']))

# convert to tuple
allocation=tuple(list(state_pop_vac_sort['total_first_allocation']))

# convert to tuple
jurisdiction=tuple(list(state_pop_vac_sort['jurisdiction']))

Top 10 states with the most population, and value on top of each bar are the total vaccine allocation

#https://matplotlib.org/examples/api/barchart_demo.html

#N=state_pop_vac_sort.jurisdiction.count()
N=10
population=tuple(list(state_pop_vac_sort['2019_population'].head(10)))
allocation=tuple(list(state_pop_vac_sort['total_first_allocation'].head(10)))
jurisdiction=tuple(list(state_pop_vac_sort['jurisdiction'].head(10)))

ind=np.arange(N)
width=0.8

fig,ax=plt.subplots(figsize=(15,8))
rects1=ax.bar(ind,population,width)#,color='#87CEFA')

ax.set_ylabel('Total Population',size=25)
ax.set_title('Total Population Per Jurisdiction',size=40)
ax.set_xlabel('Jurisdiction',size=25)
ax.set_xticks(ind)#+width/2)
ax.set_xticklabels(jurisdiction)

plt.xticks(size=15)
plt.ylim([0, 45512223])
plt.legend(fontsize='x-large',loc='upper center',title='Vaccine Allocations are in 100k',
          bbox_to_anchor=(0.6, 0.7))
# plt.ticklabel_format(style='plain',axis='y')

# attach a text label above each bar displaying each state's vaccine allocation
def autolabel(rects):
    n=-1
    for rect in rects:
        n+=1
        pop=rect.get_height() #getting the height of the text
        ax.text(rect.get_x()+rect.get_width()/2.+0.06,1.01*pop,
                'Vacc. Alloc'' ''%.2f'%(allocation[n]/100_000),
                ha='center',va='bottom',color='r',fontsize=13,rotation=20)
        
autolabel(rects1)

plt.show()

No handles with labels found to put in legend.

---

Total Population per state with the bar text been the vaccine allocation per state

#https://matplotlib.org/examples/api/barchart_demo.html

N=state_pop_vac_sort.jurisdiction.count()

population=tuple(list(state_pop_vac_sort['2019_population']))
allocation=tuple(list(state_pop_vac_sort['total_first_allocation']))
jurisdiction=tuple(list(state_pop_vac_sort['jurisdiction']))

ind=np.arange(N)
width=0.8

fig,ax=plt.subplots(figsize=(15,8))
rects1=ax.bar(ind,population,width,color='teal')

ax.set_ylabel('Total Population',size=25)
ax.set_title('Total Population Per Jurisdiction',size=40)
ax.set_xlabel('Jurisdiction',size=25)
ax.set_xticks(ind-width/2)
ax.set_xticklabels(jurisdiction)

plt.xticks(size=12,rotation=80)
plt.ylim([0, 45512223])
# plt.ticklabel_format(style='plain',axis='y')

# attach a text label above each bar displaying each state's vaccine allocation
def autolabel(rects):
    n=-1
    for rect in rects:
        n+=1
        pop=rect.get_height() #getting the height of the text
        ax.text(rect.get_x()+rect.get_width()/2.+0.06,1.01*pop,
                '%d'%(allocation[n]),
                ha='center',va='bottom',color='r',fontsize=13,rotation=20)
        
autolabel(rects1)

plt.show()

---

all the states with their total vaccine allocation, the text on each bar are allocation too

#https://matplotlib.org/examples/api/barchart_demo.html

N=state_pop_vac_sort.jurisdiction.count()
allocation=tuple(list(state_pop_vac_sort['total_first_allocation']))
jurisdiction=tuple(list(state_pop_vac_sort['jurisdiction']))

ind=np.arange(N)
width=0.8

fig,ax=plt.subplots(figsize=(15,8))
rects1=ax.bar(ind,allocation,width,color='#87CEFA')

ax.set_ylabel('Total First Allocation',size=25)
ax.set_title('Total First Allocation Per Jurisdiction',size=40)
ax.set_xlabel('Jurisdiction',size=25)
ax.set_xticks(ind+width/2)
ax.set_xticklabels(jurisdiction)

plt.xticks(rotation=80)
plt.ticklabel_format(style='plain',axis='y')

# attach a text label above each bar displaying its allocation
def autolabel(rects):
    for rect in rects:
        alloc=rect.get_height()#这一步是提取allocation的数字
        ax.text(rect.get_x()+rect.get_width()/2.,1.01*alloc,
               '%d'%int(alloc),ha='center',va='bottom',rotation=30)
autolabel(rects1)

plt.show()

state_pop_vac_sort_10=state_pop_vac_sort[:10].reset_index(drop=True)
state_pop_vac_sort_10.rename(columns={'2019_population':'population'},inplace=True)

plt.figure(figsize=(15,6))

g=sns.barplot(data=state_pop_vac_sort_10,y=state_pop_vac_sort_10['total_first_allocation'],
              x=state_pop_vac_sort_10['jurisdiction'],palette='Paired')
#              linewidth=2.5, facecolor=(1, 1, 1, 0),
#                  errcolor=".2", edgecolor=".2")

plt.ticklabel_format(style='plain',axis='y')
plt.xlabel("Jurisdiction",size=25)
plt.ylabel("Total First Allocation",size=25)
plt.title('Total First Allocation Per Jurisdiction For Top 10 States',size=30)
plt.ylim([0,4663275])
plt.xlim([-0.8,10])
plt.xticks(rotation=30,size=15)


for index, row in state_pop_vac_sort_10.iterrows():
    g.text(row.name+0.2,row.total_first_allocation+80000,
           'Pop'' ''{0:.1f}M'.format(row.population/1000_000),
           color='black',ha='center',rotation=20)
plt.savefig('../images/first_allocation_top_10_states.png')